From analytics to AI agents and beyond, real-time data solutions are becoming increasingly important in every industry. The problem is that real-time data requires real-time data streaming solutions — and these often require specialized experts to implement and maintain.
Both RisingWave and Materialize have the same goal: to make streaming data simple. But how do they differ in production? In this article, we break down the similarities, differences, and best use cases for each.
The quest to simplify streaming
A data stream processor ingests data from an intermediary message service, such as Kafka, and then transforms and shapes data for specific use cases. Data and software engineers can then access it via a serving layer like an API, or through a familiar data query language like SQL.
Traditionally, such an architecture has been difficult for most companies to implement and utilize. There are a couple of reasons for this:
- Many moving pieces: This architecture requires creating or standing up multiple robust and scalable components, such as message brokers, data transformation engines, and in-memory databases.
- Distributed system challenges: There are also serious problems around data consistency and performance that can only be solved by dedicated experts. This creates a high barrier to entry for companies with smaller tech teams that need to run real-time use cases.
- Ingestion complexity: Some implementations require accessing data through a custom API as opposed to using more accessible data serving solutions, such as SQL.
Enter data layers like RisingWave and Materialize, which aim to make streaming more accessible by providing a data processing and serving layer that solves these thorny issues out of the box. Both platforms abstract away the mechanics of streaming the data itself, and they both excel at moving computation work to the write side by handling queries when data changes.
They do this via incrementally materialized views, which automatically update the results of specific queries as new data comes in upstream — changing only the affected data, without requiring expensive full recomputation.
Understanding the core technologies
RisingWave and Materialize are both streaming database platforms designed to simplify real-time data processing.
RisingWave is a streaming database that focuses on high-throughput event ingestion and processing. It is optimized for analytics-focused streaming workloads with an emphasis on developer simplicity and rapid time-to-market.
Materialize is the live data layer for apps and AI agents that maintains the results of SQL queries over continuously changing upstream data. It is optimized for dynamic stateful workloads, offering strong consistency guarantees and full support for inserts, updates, and deletes, and helping AI agents stay in sync with reality.
Similarities between RisingWave and Materialize
- Distributed engines: Both Materialize and RisingWave are natively distributed platforms.
- Developer accessibility: Both are PostgreSQL wire-compatible. Developers can work in familiar SQL syntax with no need to learn new APIs, dialects, or frameworks.
- High-throughput ingestion: Both can process millions of events per second with efficient resource utilization.
- Incremental view maintenance: Both update query results as upstream data changes, maintaining current views without expensive recomputation.
RisingWave: Developer-first streaming database
RisingWave is well-suited for organizations looking to quickly implement streaming analytics capabilities, particularly for use cases like real-time dashboards, event-driven applications, and feature engineering for machine learning pipelines. Its focus on developer experience makes it accessible to teams without specialized streaming expertise.
- Streaming analytics focus: Optimized for analytical workloads over event streams, particularly time-series and aggregation use cases
- No transaction management: RisingWave doesn’t ensure the ordering of writes, which would require a centralized transaction manager or timestamp oracle
- Open-source foundation: Apache 2.0 license with active community development and transparent roadmap
Materialize: Enterprise-grade live data layer
Materialize’s primary focus is operational intelligence and real-time data products. This makes it ideal for applications like fraud detection systems, supply chain visibility, customer-facing analytics, regulatory compliance monitoring, and digital twin architectures for AI agents.
Materialize delivers critical enterprise capabilities:
- Strong consistency: Guarantees strict-serializable consistency across all data sources and queries, essential for operational workloads
- Complex real-time queries: Supports multi-way joins, aggregations, window functions, recursive CTEs, and sophisticated SQL operations on live data
- Millisecond latency: Single-millisecond response times for querying pre-computed results
- Enterprise data integration: Robust connectors and Change Data Capture (CDC) support for seamless integration with existing data infrastructure
- AI-ready architecture: Purpose-built to support agentic AI applications requiring live, semantically rich business context
Materialize vs RisingWave: Comparative Analysis
Both platforms aim to democratize real-time data processing. Despite the architectural similarities, there are differences in the way each system is designed, built, and tuned — differences that ultimately make a real difference in consistency, performance, and workload suitability.
Materialized views & consistency models
Both platforms support materialized views as their core abstraction, but with differences in capabilities and guarantees.
Materialize: Real-time consistency
Materialize provides strong consistency guarantees, enforcing strict-serializable consistency across all sources and queries. Materialized’s strong consistency is essential because, under Materialize’s performance model, new inputs are applied incrementally without full recomputation. Even though there may be many moving parts when a query comes in, Materialize ensures that the numbers are always going to be strictly correct.
- Queries reflect a consistent snapshot of all input data at a specific logical timestamp.
- Updates and deletes are handled incrementally and applied in the correct transactional order.
- Multi-stream joins produce correct results without requiring users to manage timing or coordination.
Strong, strict-serializable consistency makes Materialize appropriate for workloads where correctness under change is required, including operational analytics, data products, and embedded real-time business logic.
In Materialize, the data engine is focused on consistency and building out composable data products where you can have views stacked on top of each other, join them together, and create your ideal custom analytics dashboard. With strong consistency out of the box, you know that if you have multiple items on your visualization, say two summaries of your data that break it out in different ways, Materialize will guarantee that when the page loads, they’re both showing you results as of the exact same input datasets. Unlike other systems where different frequencies of ETLs can produce a lot of inconsistency, Materialize’s data will always tie out.
It also makes Materialize an ideal data layer for AI agents and MCPs — one they can access without taxing production systems, and with consistency guarantees all the way across. This emerging digital twin architecture makes it possible to have an exact, always-current model of your relevant business entities and their relationships, expressed in the language of your company (customers, orders, suppliers, routes) rather than low-level tables, that AI agents can query against. Materialize’s strong consistency helps agentic AI applications stay in sync with reality by immediately reflecting any changes that happen as a consequence of an action taken by the agent.
- All materialized views maintain strict-serializable consistency across concurrent updates. Once defined, Materialize maintains views incrementally (via change data capture) with strong consistency guarantees. Results update in response to data changes while preserving transactional correctness across all dependent views.
- Materialize’s core stream processing library is Timely Dataflow, the mature and stable open source software originally developed at Microsoft Research starting more than a decade ago. Materialize utilizes Timely Dataflow to automatically manage consistency across complex view hierarchies, without requiring developers to grasp concepts like watermarks or out of order concerns.
RisingWave: Focus on streaming analytics patterns.
RisingWave’s materialized view support is primarily designed for analytical workloads rather than operational systems. RisingWave provides snapshot read consistency, which means that when running a query against materialized views or tables in the same database, the result is guaranteed to be consistent between those tables and materialized views.
Unlike Materialize, which natively handles updates and deletes incrementally and applies them in the correct transactional order, RisingWave requires users to define and manage timing and coordination to keep multi-stream results accurate and concurrent. Otherwise, by default, RisingWave’s snapshot consistency reflects input data in order of timestamp, regardless of query logic or the data’s relationship to the current view.
While effective for many streaming analytics use cases, RisingWave’s snapshot consistency may have limitations for complex operational scenarios requiring sophisticated consistency guarantees.
The upshot: Materialize’s strong consistency makes it suitable for mission-critical operational workloads where correctness is non-negotiable, including financial systems, regulatory reporting, and AI agent architectures requiring accurate real-time business context.
RisingWave implements an eventual-consistency model optimized for streaming analytics. Designed for high-throughput scenarios where eventual consistency is acceptable, RisingWave may sacrifice some consistency guarantees to achieve higher throughput. This approach works well for many streaming analytics use cases where approximate results or slight delays are acceptable, but it may not be suitable for operational systems requiring strict consistency guarantees.
Data mutability and change handling
- Materialize excels at handling mutable data with full support for inserts, updates, and deletes. It offers complete CRUD support for native handling of all data modification operations and identity tracking for maintaining data record identity to support correct incremental updates.
- RisingWave focuses primarily on append-mostly workloads typical of streaming analytics. RisingWave handles all data operations with a Log Structured Merge (LSM) Tree storage model in an append-only manner. This design is particularly well-suited for time-series data, event-driven analytics, and similar scenarios where data is primarily added, rather than frequently updated or deleted.
Performance Models
- Materialize implements an “always-ready” performance model optimized for operational workloads. Query results are continuously maintained and immediately available, with single-digit millisecondresponse times for materialized views regardless of data volume. Materialize’s performance depends on data change volume, not total data size, and it is designed to handle LLM-scale workloads — massive query volumes from AI agents and automated systems
- RisingWave implements a performance model focused on high-throughput ingestion, optimized for processing millions of events per second. RisingWave is designed for resource efficiency and cost-effective processing of large event volumes, so its performance characteristics are tuned for efficient continuous data processing rather than lowest possible latency.
Enterprise readiness and operational maturity
RisingWave was one of the first simplified streaming solutions to market. With both open-source and cloud solutions available, it’s easy to get started quickly and enjoy relative success with the platform.
Materialize has worked for years with some of the leading researchers in the industry to build a platform that delivers strongly consistent materialized views with sub-millisecond performance. And the company is committed to pushing those gains even further: For example, Materialize’s recent 2.52 release delivers a 13% boost in speed while using 7% less memory.
Materialize provides enterprise-grade production and operational capabilities, including advanced monitoring (with comprehensive observability tools and freshness dashboards) and enterprise security features like role-based access control.
Use cases for RisingWave vs Materialize
RisingWave and Materialize both address the need for accessible real-time data processing, but they serve different somewhat purposes in modern data architectures.
RisingWave lists a number of use cases it aims to support, which include:
- Streaming analytics. Store and query highly volatile data, such as stock prices or venue capacity, to make real-time decisions (e.g., selling or trading stock).
- Event-driven applications. Applications like fraud detection systems, which analyze purchases as they happen and flag suspicious purchases before they go through.
- Real-time data enrichment. Performing personalization on the fly based on a user’s actions on a website - e.g., serving custom discounts based on their browsing behavior.
- Feature engineering. Creating vector embeddings from data for machine learning and AI models, enabling real-time inference for situations such as making real-time bids for ads.
Materialize covers all of these use cases, as well as:
- Digital twins for AI agents. AI agents are AI-powered processes that process data and make decisions autonomously, absent human input. By definition, these agents can’t rely on ETL processes that run every six hours — their function utterly depends on the current state of reality. Using Materialize, you can create a digital twin that provides agents with tight feedback loops, enabling them to complete tasks faster and more reliably.
- OLTP and data warehouse offload. Many companies are running real-time workloads on transactional or analytical databases that weren’t built to run them. This drags down the performance of critical operations, such as e-commerce and reporting. Thanks to its PostgreSQL wire compatibility, it’s easy to move these workloads downstream to Materialize.
- Manufacturing. Analyze and detect manufacturing floor anomalies and deliver real-time notifications so that operators can fix problems before they cause an expensive work stoppage.
- Logistics. Enable delivery tracking and notification to customers with data teams as small as a single person and at a fraction of the cost of a custom streaming solution.
When to choose RisingWave
RisingWave provides an accessible entry point into streaming data processing with strong developer experience and effective streaming analytics capabilities. Use cases include:
- Streaming analytics: Real-time dashboards and reporting over event streams
- Event-driven applications: Applications processing continuous event streams
- Developer-first scenarios: Teams prioritizing rapid development and deployment
- Analytics-focused workloads: Use cases where eventual consistency is acceptable
Choose RisingWave when you need:
- High-throughput streaming analytics over event data where eventual consistency is acceptable
- Developer-first platform with rapid time-to-value
- Cost-effective processing of large event volumes is more important than lowest possible query latencies
- Open-source foundation with community development
- Analytics-focused streaming applications
When to choose Materialize
Materialize is purpose-built for operational intelligence where consistency, reliability, and sophisticated query capabilities are paramount. It excels at efficiently maintaining always-current views of your data while providing enterprise-grade operational characteristics. Use cases include:
- Operational intelligence: Mission-critical systems requiring strong consistency
- AI agent architectures: Digital twins and real-time context for autonomous systems
- Complex real-time views: Sophisticated SQL operations over changing data
- Enterprise data products: Customer-facing analytics and operational dashboards
- Database offloading: Moving real-time workloads from OLTP systems
Choose Materialize when you need:
- Strong consistency guarantees for operational workloads
- Complex real-time SQL operations over changing data
- Enterprise-grade reliability and operational maturity
- Real-time data products for customer-facing applications
- Digital twin architectures for AI agents and autonomous systems
Appendix 1: Feature Comparison Matrix
| Requirement | Materialize | RisingWave | Comments |
| Streaming Data Support | Native, comprehensive | Native, analytics-focused | Both provide strong streaming capabilities |
| Strong Consistency | Strict-serializable | Eventual consistency | Critical difference for operational workloads |
| Complex SQL Support | Full PostgreSQL compatibility | Good SQL support | Materialize supports more advanced features |
| Materialized Views | Same | Same | Different optimization targets |
| Change Data Capture | Native, robust | Supported | Materialize provides more comprehensive CDC |
| Enterprise Features | Comprehensive | Growing | Materialize more mature for enterprise deployment |
| AI Agent Support | Purpose-built | Not specialized | Materialize designed for agentic AI workloads |
| High-Throughput Analytics | Adequate | Optimized | RisingWave optimized for analytical throughput |
| Developer Experience | PostgreSQL-native | Developer-first | Both prioritize ease of use |
| Operational Maturity | Enterprise-ready | Production-capable | Materialize more mature for mission-critical use |
Appendix 2: Licensing and Ecosystem
- Materialize is source-available under the BSL license with a free Community Edition. Enterprise features require commercial licensing or Materialize Cloud.
- RisingWave is fully open source under the Apache 2.0 license with self-hosted and cloud deployment options.
Both platforms offer mature SQL interfaces, growing ecosystem integrations, and active development communities, but Materialize provides more comprehensive enterprise support and operational tooling.